As we enter a new era of quantum technology, it is increasingly important to develop methods to aid in the accurate preparation of quantum states for a variety of materials, matter, and devices. Computational techniques can be used to reconstruct a state from data, however the growing number of qubits demands ongoing algorithmic advances in order to keep pace with experiments. In this paper, we present an open-source software package called QuCumber that uses machine learning to reconstruct a quantum state consistent with a set of projective measurements. QuCumber uses a restricted Boltzmann machine to efficiently represent the quantum wavefunction for a large number of qubits. New measurements can be generated from the machine to obtain physical observables not easily accessible from the original data.
@article{arxiv.1812.09329,
title = {QuCumber: wavefunction reconstruction with neural networks},
author = {Matthew J. S. Beach and Isaac De Vlugt and Anna Golubeva and Patrick Huembeli and Bohdan Kulchytskyy and Xiuzhe Luo and Roger G. Melko and Ejaaz Merali and Giacomo Torlai},
journal= {arXiv preprint arXiv:1812.09329},
year = {2019}
}